Today, machine learning (ML) has become a buzzword that’s thrown around a lot. I see it almost everywhere in a tagline, article, or commercial. If “unprecedented” was the word of 2020, machine learning might be this decade’s catchphrase. But, what is it? From what I’ve learned working in and around startups for the past several years, defining machine learning is as complex or simple as you like, depending on how deep you go. It’s been a while since I was on the swim team so let’s stay in the shallow end while figuring out what it is.
Machine learning as a category falls under the umbrella of artificial intelligence (AI). So first, what is AI? In simple terms, AI is when a machine has the facility to act with human intelligence. Cue the 2001 Space Odyssey soundtrack. Don’t worry, generally speaking, AI is designed with the consumer in mind and usually for specific fields of knowledge or purposes. You use AI more than you probably think. Apple’s Siri, Amazon’s Alexa, and Google Home are three obvious AI examples but GPS apps like Waze and Google Maps use a ton of AI, too. If your phone opens with facial recognition or a thumbprint, you’re using AI every day. The camera on your phone? Lots of AI involved there. It’s almost impossible to avoid artificial intelligence and you shouldn’t want to. These complex algorithms were designed to do one thing: help you.
Artificial intelligence uses machine learning techniques to make your day easier. By using algorithms, it teaches computers to learn naturally without specific programming. These algorithms are data-driven at their core. Without good data, your ML model will not be strong. Here’s the basic three-step process that ML follows:
- Collect Data: Data comes first in the world of machine learning. Relevant data needs to be organized in a way that a computer can understand it before it’s put into a machine learning model. This first step is integral to having good results at the end. You’ve probably heard the saying: garbage in, garbage out.
- Build Your Model: At this point, we’re going to need a data scientist. They are going to design and pick the chosen machine learning algorithm for the task at hand.
- Train and Deploy: Clean data will train your model if done right. Predictive, accurate outputs are the goal and with a well-written algorithm and relevant data, they’re attainable.
This process sounds simple but it takes years of studying and education to be able to do it. Recurrency’s data scientists and engineers have PhDs and years of experience for this reason. We want to make sure our density of talent is high.
Types of Machine Learning
There are three main types of ML: supervised, unsupervised, and reinforced.
- Supervised: This is when a machine learning algorithm uses labeled data. With this tactic, you know what the target prediction is. With a list of data, you are trying to predict the last data set by using all the previous data. Because the machine has the final answer, it is able to self-train to get there. For example, if a computer has images labeled as cats, it can train itself to correctly identify other images of cats.
- Unsupervised: An ML model using unlabeled data is called unsupervised machine learning. During this process, the model is given data sets but told to find the patterns unassisted. The benefit to this model is that it can identify patterns you wouldn’t have known to look for on your own. Unsupervised ML has been used to find evolutionary relationships while looking at sets of DNA over time.
- Reinforced: A reinforcement machine learning model is the classic story of trial and error. The computer will adapt to a rewards system and use it to choose the right actions. Self-driving cars use this ML model to learn how to drive. When the car makes the right decisions on the road, the computer is rewarded.
Here at Recurrency we use supervised and unsupervised machine learning models for our four core predictive features: Dynamic Pricing, Demand Forecasting, Customer Reorder Prediction, and Upselling Recommendations. The Recurrency platform uses this modeling to give intelligent business suggestions to support both your sales and purchasing teams. Our ML algorithms are carefully tested and formatted to make them reliable, with constant attention and ‘learning’ to continually reinforce. With Recurrency, you don’t have to second guess.
A Bright Future for AI
Machine learning is being integrated into almost every business process. That being said, I’m not saying every industry needs it. But some definitely do. Take the distribution industry, which is in desperate need of innovation. If machine learning requires a lot of data, Enterprise Resource Planning (ERP) systems seem to be the perfect match. They are data-packed and (mostly) well organized. But ERPs don’t do anything with your data except organize it, and they won’t tell you anything you don’t already know.
This is where Recurrency steps in. Recurrency’s automated platform will leverage our machine learning algorithms to make your data work for you. We pull your historical data into our models (trained on both your data as well as a number of public data sets that we have access to) to find the relevant patterns that help control your bottom line, boost efficiency, and increase your profitability. Wondering who your sales reps should be calling? Recurrency tells you who is due to reorder based on a number of factors! Oh, and we’ll also tell you what they should be ready to buy again, and even give some suggestions for other items that they’re probably buying from your competitor instead. Instead of dumpster diving for data in your ERP, Recurrency uses the automation that AI provides to give you exactly what you’re looking for.
The sun is rising on machine learning and it will leave some behind in the dark. Start your day by booking a demo.